How we used Databricks to move from manual financial workflows to automated, AI-augmented decision pipelines. 

At Systech, our journey with data and analytics often starts with helping enterprises modernize their pipelines and unify data. But increasingly, we’re seeing a shift: clients are looking for more than just dashboards or reports — they want AI-powered operational intelligence. They want systems that assist with decision-making, automate repetitive tasks, and learn over time. And they want it to work within the framework they already trust. 

That’s where Databricks Lakehouse plays a critical role in how we bring practical AI into enterprise processes. 

The Real-World Problem: Expense Reclassification at Scale 

We recently worked with a large real estate investment trust (REIT) managing thousands of properties. Each month, their Finance team would manually process over 20 reports from systems like Yardi, CRM, and SSRS to categorize property-level expenses. This involved mapping transactions, applying threshold logic, and identifying what to capitalize versus expense — all through manually maintained spreadsheets and macros. 

The work was complex, time-intensive, and error-prone. On average, the process took 2–3 full days every month. 

Our goal was to bring this into a governed, automated, and scalable framework using Databricks — and then build intelligence into the workflow to help the team make better, faster decisions. 

Laying the Foundation with Databricks 

We began by modernizing the data pipeline on Databricks Lakehouse: 

• Data pulled from Bronze/Silver layers within the enterprise data warehouse 

• Transformation logic implemented in Spark and SQL, reflecting complex reclassification rules 

• Curation of gold layer datasets using Delta Lake for traceability and reuse 

• Validation rules and outputs for downstream consumption and audit 

This alone cut down monthly effort by more than 70%, increased consistency, and provided visibility across months and teams. 

Where AI Enters the Workflow 

Having established a stable data foundation, we introduced AI where human rules fell short — particularly in interpreting ambiguous financial descriptions. 

A key challenge was interpreting free-text fields like transaction “Remarks” and “Descriptions.” These often included vendor-specific phrases, shorthand, or ambiguous notes that influenced how a transaction should be classified. Rather than creating hundreds of string match rules, we applied embedding-based similarity scoring using OpenAI models hosted securely — helping us surface the most likely category based on historical context. 

Finance reviewers retained final control, but the system could now flag exceptions, suggest classifications, and learn from each correction. This human-in-the-loop feedback loop steadily improved precision and reduced fatigue. 

Why Databricks Was the Right Choice 

Databricks enabled us to do this efficiently because of its ability to combine: 

• Unified data and compute — ingestion, transformation, ML inference all within one environment 

• Scalable Spark processing — ideal for month-end bulk processing across large property datasets 

• Governance and audit — tracking every rule, transformation, and suggestion with lineage and permissions 

• AI-readiness — native integration with Azure OpenAI within Databricks notebooks enabled secure and scalable LLM-driven classification 

Key Outcomes 

Beyond just automation, this created an AI-ready foundation for the client’s broader financial operations — from insights to audit support. 

Looking Forward 

At Systech, we see AI not as a separate stream, but as an extension of intelligent data engineering. The work doesn’t start with models; it starts with clean, contextual, and governed data — something Databricks helps us deliver at scale. 

As we continue to deepen our collaboration with Databricks, our focus is on operationalizing AI across more domains: finance, customer service, supply chain, and field operations. We’re investing in skill-building, certifications, and frameworks that allow us to deliver production-grade AI — fast, secure, and explainable. 

Because for AI to work in the enterprise, it needs to be usable. And for that, it needs to be built right — from the data up. 

About Systech 

Systech Solutions is a global data and analytics consulting firm with over 30 years of experience helping enterprises unlock the value of their data. As a Databricks partner, we specialize in building AI-powered, cloud-native platforms using the Lakehouse architecture. Our expertise spans data engineering, machine learning, GenAI, and enterprise-scale modernization. We enable clients across industries to make faster, smarter decisions through intelligent data solutions. Interested in AI-led operational intelligence? Let’s connect to explore how we can help modernize your finance, supply chain, or customer operations.